Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations104651
Missing cells16211
Missing cells (%)0.6%
Duplicate rows2565
Duplicate rows (%)2.5%
Total size in memory71.2 MiB
Average record size in memory712.9 B

Variable types

Numeric13
Text3
Categorical6
Boolean1
DateTime1

Alerts

country has constant value "UNITED STATES"Constant
country code has constant value "US"Constant
Dataset has 2565 (2.5%) duplicate rowsDuplicates
long is highly overall correlated with neighbourhood groupHigh correlation
neighbourhood group is highly overall correlated with longHigh correlation
number of reviews is highly overall correlated with reviews per monthHigh correlation
price_clean is highly overall correlated with service fee_cleanedHigh correlation
reviews per month is highly overall correlated with number of reviewsHigh correlation
service fee_cleaned is highly overall correlated with price_cleanHigh correlation
last review_date has 16206 (15.5%) missing valuesMissing
number of reviews has 16045 (15.3%) zerosZeros
availability 365 has 24007 (22.9%) zerosZeros

Reproduction

Analysis started2025-10-21 17:26:14.628852
Analysis finished2025-10-21 17:26:58.405362
Duration43.78 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct102058
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29144003
Minimum1001254
Maximum57367417
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size817.7 KiB
2025-10-21T17:26:58.538502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1001254
5-th percentile3834911
Q115088024
median29134946
Q343188496
95-th percentile54528319
Maximum57367417
Range56366163
Interquartile range (IQR)28100472

Descriptive statistics

Standard deviation16253954
Coefficient of variation (CV)0.55771179
Kurtosis-1.1974283
Mean29144003
Median Absolute Deviation (MAD)14051064
Skewness0.0030807631
Sum3.049949 × 1012
Variance2.6419102 × 1014
MonotonicityNot monotonic
2025-10-21T17:26:58.684534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
203876184
 
< 0.1%
203080873
 
< 0.1%
203401213
 
< 0.1%
355896763
 
< 0.1%
355234003
 
< 0.1%
203417773
 
< 0.1%
355880193
 
< 0.1%
203467483
 
< 0.1%
355416263
 
< 0.1%
203163723
 
< 0.1%
Other values (102048)104620
> 99.9%
ValueCountFrequency (%)
10012541
< 0.1%
10021021
< 0.1%
10024031
< 0.1%
10027551
< 0.1%
10036891
< 0.1%
10040981
< 0.1%
10046501
< 0.1%
10052021
< 0.1%
10057541
< 0.1%
10063071
< 0.1%
ValueCountFrequency (%)
573674171
< 0.1%
573668651
< 0.1%
573663131
< 0.1%
573657601
< 0.1%
573652081
< 0.1%
573646561
< 0.1%
573641031
< 0.1%
573635511
< 0.1%
573629991
< 0.1%
573624461
< 0.1%

NAME
Text

Distinct60662
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
2025-10-21T17:26:59.132124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length248
Median length147
Mean length37.522527
Min length1

Characters and Unicode

Total characters3926770
Distinct characters949
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31282 ?
Unique (%)29.9%

Sample

1st rowCLASSIC ARTIST LOFT
2nd rowÉTAGE AU SEIN D’UN DUPLEX À BROOKLYN
3rd rowAMAZING VIEW IN ULTRA LUXURY MIDTOWN APARTMENT!
4th rowHARLEM HOME AWAY FROM HOME!
5th rowCOZY LOFT IN FLUSHING
ValueCountFrequency (%)
in35567
 
5.5%
room21700
 
3.3%
18290
 
2.8%
bedroom16434
 
2.5%
private15851
 
2.4%
apartment14395
 
2.2%
cozy10761
 
1.7%
apt9533
 
1.5%
studio8856
 
1.4%
brooklyn8760
 
1.3%
Other values (16029)489507
75.3%
2025-10-21T17:26:59.724070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
548352
14.0%
E304854
 
7.8%
O287448
 
7.3%
A267062
 
6.8%
T255331
 
6.5%
R251416
 
6.4%
N230792
 
5.9%
I219715
 
5.6%
S160398
 
4.1%
L141711
 
3.6%
Other values (939)1259691
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)3926770
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
548352
14.0%
E304854
 
7.8%
O287448
 
7.3%
A267062
 
6.8%
T255331
 
6.5%
R251416
 
6.4%
N230792
 
5.9%
I219715
 
5.6%
S160398
 
4.1%
L141711
 
3.6%
Other values (939)1259691
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3926770
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
548352
14.0%
E304854
 
7.8%
O287448
 
7.3%
A267062
 
6.8%
T255331
 
6.5%
R251416
 
6.4%
N230792
 
5.9%
I219715
 
5.6%
S160398
 
4.1%
L141711
 
3.6%
Other values (939)1259691
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3926770
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
548352
14.0%
E304854
 
7.8%
O287448
 
7.3%
A267062
 
6.8%
T255331
 
6.5%
R251416
 
6.4%
N230792
 
5.9%
I219715
 
5.6%
S160398
 
4.1%
L141711
 
3.6%
Other values (939)1259691
32.1%

host id
Real number (ℝ)

Distinct102057
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9259364 × 1010
Minimum1.2360052 × 108
Maximum9.8763129 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size817.7 KiB
2025-10-21T17:26:59.865354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.2360052 × 108
5-th percentile4.9222327 × 109
Q12.4609577 × 1010
median4.9106518 × 1010
Q37.399603 × 1010
95-th percentile9.3820735 × 1010
Maximum9.8763129 × 1010
Range9.8639529 × 1010
Interquartile range (IQR)4.9386452 × 1010

Descriptive statistics

Standard deviation2.8532715 × 1010
Coefficient of variation (CV)0.57923434
Kurtosis-1.201732
Mean4.9259364 × 1010
Median Absolute Deviation (MAD)2.4704505 × 1010
Skewness0.0071010287
Sum5.1550417 × 1015
Variance8.1411582 × 1020
MonotonicityNot monotonic
2025-10-21T17:27:00.025833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11654907794
 
< 0.1%
9.752824846 × 10103
 
< 0.1%
4.072550887 × 10103
 
< 0.1%
2.103552879 × 10103
 
< 0.1%
9.145632944 × 10103
 
< 0.1%
8.946153186 × 10103
 
< 0.1%
5.759150292 × 10103
 
< 0.1%
1.212779805 × 10103
 
< 0.1%
62683164123
 
< 0.1%
4.6677963 × 10103
 
< 0.1%
Other values (102047)104620
> 99.9%
ValueCountFrequency (%)
1236005181
< 0.1%
1240396481
< 0.1%
1244726191
< 0.1%
1297565651
< 0.1%
1303496121
< 0.1%
1305934311
< 0.1%
1316020891
< 0.1%
1322383051
< 0.1%
1332647401
< 0.1%
1344521201
< 0.1%
ValueCountFrequency (%)
9.876312902 × 10101
< 0.1%
9.876268323 × 10101
< 0.1%
9.876266181 × 10101
< 0.1%
9.876096863 × 10101
< 0.1%
9.875813627 × 10101
< 0.1%
9.875797561 × 10101
< 0.1%
9.875795011 × 10101
< 0.1%
9.875764603 × 10101
< 0.1%
9.87574594 × 10101
< 0.1%
9.875733136 × 10101
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
UNCONFIRMED
52495 
VERIFIED
52156 

Length

Max length11
Median length11
Mean length9.504859
Min length8

Characters and Unicode

Total characters994693
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVERIFIED
2nd rowUNCONFIRMED
3rd rowVERIFIED
4th rowVERIFIED
5th rowVERIFIED

Common Values

ValueCountFrequency (%)
UNCONFIRMED52495
50.2%
VERIFIED52156
49.8%

Length

2025-10-21T17:27:00.600027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-21T17:27:00.677051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
unconfirmed52495
50.2%
verified52156
49.8%

Most occurring characters

ValueCountFrequency (%)
I156807
15.8%
E156807
15.8%
N104990
10.6%
D104651
10.5%
F104651
10.5%
R104651
10.5%
U52495
 
5.3%
C52495
 
5.3%
O52495
 
5.3%
M52495
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)994693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I156807
15.8%
E156807
15.8%
N104990
10.6%
D104651
10.5%
F104651
10.5%
R104651
10.5%
U52495
 
5.3%
C52495
 
5.3%
O52495
 
5.3%
M52495
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)994693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I156807
15.8%
E156807
15.8%
N104990
10.6%
D104651
10.5%
F104651
10.5%
R104651
10.5%
U52495
 
5.3%
C52495
 
5.3%
O52495
 
5.3%
M52495
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)994693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I156807
15.8%
E156807
15.8%
N104990
10.6%
D104651
10.5%
F104651
10.5%
R104651
10.5%
U52495
 
5.3%
C52495
 
5.3%
O52495
 
5.3%
M52495
 
5.3%
Distinct13155
Distinct (%)12.6%
Missing5
Missing (%)< 0.1%
Memory size5.5 MiB
2025-10-21T17:27:00.973783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length32
Mean length6.1777325
Min length1

Characters and Unicode

Total characters646475
Distinct characters180
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3839 ?
Unique (%)3.7%

Sample

1st rowJOSH
2nd rowSOPHIE & GRÉGOIRE
3rd rowMAY
4th rowARIELLE
5th rowERJON
ValueCountFrequency (%)
2379
 
2.0%
michael1419
 
1.2%
and1414
 
1.2%
david866
 
0.7%
sonder755
 
0.6%
alex687
 
0.6%
john671
 
0.6%
nyc563
 
0.5%
laura548
 
0.5%
daniel525
 
0.4%
Other values (11709)107616
91.6%
2025-10-21T17:27:01.485906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A94874
14.7%
E67156
 
10.4%
N56217
 
8.7%
I54527
 
8.4%
R43975
 
6.8%
L39460
 
6.1%
S30176
 
4.7%
O28947
 
4.5%
T24614
 
3.8%
M23062
 
3.6%
Other values (170)183467
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)646475
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A94874
14.7%
E67156
 
10.4%
N56217
 
8.7%
I54527
 
8.4%
R43975
 
6.8%
L39460
 
6.1%
S30176
 
4.7%
O28947
 
4.5%
T24614
 
3.8%
M23062
 
3.6%
Other values (170)183467
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)646475
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A94874
14.7%
E67156
 
10.4%
N56217
 
8.7%
I54527
 
8.4%
R43975
 
6.8%
L39460
 
6.1%
S30176
 
4.7%
O28947
 
4.5%
T24614
 
3.8%
M23062
 
3.6%
Other values (170)183467
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)646475
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A94874
14.7%
E67156
 
10.4%
N56217
 
8.7%
I54527
 
8.4%
R43975
 
6.8%
L39460
 
6.1%
S30176
 
4.7%
O28947
 
4.5%
T24614
 
3.8%
M23062
 
3.6%
Other values (170)183467
28.4%

neighbourhood group
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
MANHATTAN
44701 
BROOKLYN
42679 
QUEENS
13524 
BRONX
 
2774
STATEN ISLAND
 
971
Other values (2)
 
2

Length

Max length13
Median length9
Mean length8.1355458
Min length5

Characters and Unicode

Total characters851393
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowBROOKLYN
2nd rowBROOKLYN
3rd rowMANHATTAN
4th rowMANHATTAN
5th rowQUEENS

Common Values

ValueCountFrequency (%)
MANHATTAN44701
42.7%
BROOKLYN42679
40.8%
QUEENS13524
 
12.9%
BRONX2774
 
2.7%
STATEN ISLAND971
 
0.9%
BROOKLN1
 
< 0.1%
MANHATAN1
 
< 0.1%

Length

2025-10-21T17:27:01.616048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-21T17:27:01.716431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
manhattan44701
42.3%
brooklyn42679
40.4%
queens13524
 
12.8%
bronx2774
 
2.6%
staten971
 
0.9%
island971
 
0.9%
brookln1
 
< 0.1%
manhatan1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N150324
17.7%
A136048
16.0%
T91345
10.7%
O88134
10.4%
R45454
 
5.3%
B45454
 
5.3%
M44702
 
5.3%
H44702
 
5.3%
L43651
 
5.1%
K42680
 
5.0%
Other values (9)118899
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)851393
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N150324
17.7%
A136048
16.0%
T91345
10.7%
O88134
10.4%
R45454
 
5.3%
B45454
 
5.3%
M44702
 
5.3%
H44702
 
5.3%
L43651
 
5.1%
K42680
 
5.0%
Other values (9)118899
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)851393
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N150324
17.7%
A136048
16.0%
T91345
10.7%
O88134
10.4%
R45454
 
5.3%
B45454
 
5.3%
M44702
 
5.3%
H44702
 
5.3%
L43651
 
5.1%
K42680
 
5.0%
Other values (9)118899
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)851393
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N150324
17.7%
A136048
16.0%
T91345
10.7%
O88134
10.4%
R45454
 
5.3%
B45454
 
5.3%
M44702
 
5.3%
H44702
 
5.3%
L43651
 
5.1%
K42680
 
5.0%
Other values (9)118899
14.0%
Distinct224
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
2025-10-21T17:27:02.038701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length18
Mean length11.870828
Min length4

Characters and Unicode

Total characters1242294
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCOLUMBIA ST
2nd rowCLINTON HILL
3rd rowHELL'S KITCHEN
4th rowHARLEM
5th rowFLUSHING
ValueCountFrequency (%)
east14061
 
8.3%
side9678
 
5.7%
bedford-stuyvesant8110
 
4.8%
harlem7966
 
4.7%
williamsburg7918
 
4.7%
upper7694
 
4.6%
heights7475
 
4.4%
village6181
 
3.7%
west5521
 
3.3%
bushwick5089
 
3.0%
Other values (236)89097
52.8%
2025-10-21T17:27:02.487924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E128938
 
10.4%
S109449
 
8.8%
I90734
 
7.3%
T84327
 
6.8%
A83853
 
6.7%
L76962
 
6.2%
R74551
 
6.0%
64139
 
5.2%
N57924
 
4.7%
H56880
 
4.6%
Other values (21)414537
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1242294
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E128938
 
10.4%
S109449
 
8.8%
I90734
 
7.3%
T84327
 
6.8%
A83853
 
6.7%
L76962
 
6.2%
R74551
 
6.0%
64139
 
5.2%
N57924
 
4.7%
H56880
 
4.6%
Other values (21)414537
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1242294
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E128938
 
10.4%
S109449
 
8.8%
I90734
 
7.3%
T84327
 
6.8%
A83853
 
6.7%
L76962
 
6.2%
R74551
 
6.0%
64139
 
5.2%
N57924
 
4.7%
H56880
 
4.6%
Other values (21)414537
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1242294
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E128938
 
10.4%
S109449
 
8.8%
I90734
 
7.3%
T84327
 
6.8%
A83853
 
6.7%
L76962
 
6.2%
R74551
 
6.0%
64139
 
5.2%
N57924
 
4.7%
H56880
 
4.6%
Other values (21)414537
33.4%

lat
Real number (ℝ)

Distinct21992
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.728072
Minimum40.49979
Maximum40.91697
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size817.7 KiB
2025-10-21T17:27:02.623999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum40.49979
5-th percentile40.64318
Q140.68874
median40.72228
Q340.76276
95-th percentile40.82669
Maximum40.91697
Range0.41718
Interquartile range (IQR)0.07402

Descriptive statistics

Standard deviation0.05589175
Coefficient of variation (CV)0.0013723151
Kurtosis0.1501276
Mean40.728072
Median Absolute Deviation (MAD)0.03673
Skewness0.22915667
Sum4262233.5
Variance0.0031238877
MonotonicityNot monotonic
2025-10-21T17:27:02.773607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.7641136
 
< 0.1%
40.7181332
 
< 0.1%
40.7612529
 
< 0.1%
40.7375628
 
< 0.1%
40.7135326
 
< 0.1%
40.7610625
 
< 0.1%
40.724425
 
< 0.1%
40.689825
 
< 0.1%
40.6853724
 
< 0.1%
40.7071923
 
< 0.1%
Other values (21982)104378
99.7%
ValueCountFrequency (%)
40.499791
< 0.1%
40.504561
< 0.1%
40.506411
< 0.1%
40.507082
< 0.1%
40.508632
< 0.1%
40.508682
< 0.1%
40.508732
< 0.1%
40.509431
< 0.1%
40.511332
< 0.1%
40.522112
< 0.1%
ValueCountFrequency (%)
40.916971
< 0.1%
40.916851
< 0.1%
40.91311
< 0.1%
40.913061
< 0.1%
40.912481
< 0.1%
40.912341
< 0.1%
40.911692
< 0.1%
40.911671
< 0.1%
40.91161
< 0.1%
40.911391
< 0.1%

long
Real number (ℝ)

High correlation 

Distinct17774
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.949641
Minimum-74.24984
Maximum-73.70522
Zeros0
Zeros (%)0.0%
Negative104651
Negative (%)100.0%
Memory size817.7 KiB
2025-10-21T17:27:02.913123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-74.24984
5-th percentile-74.00387
Q1-73.98258
median-73.95445
Q3-73.93234
95-th percentile-73.85112
Maximum-73.70522
Range0.54462
Interquartile range (IQR)0.05024

Descriptive statistics

Standard deviation0.049534423
Coefficient of variation (CV)-0.00066983994
Kurtosis4.3308443
Mean-73.949641
Median Absolute Deviation (MAD)0.02596
Skewness1.242063
Sum-7738903.9
Variance0.0024536591
MonotonicityNot monotonic
2025-10-21T17:27:03.071702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.9937144
 
< 0.1%
-73.953541
 
< 0.1%
-73.9479137
 
< 0.1%
-73.9542737
 
< 0.1%
-73.9497735
 
< 0.1%
-73.9567734
 
< 0.1%
-73.9567533
 
< 0.1%
-73.9451333
 
< 0.1%
-73.94332
 
< 0.1%
-73.9573232
 
< 0.1%
Other values (17764)104293
99.7%
ValueCountFrequency (%)
-74.249841
< 0.1%
-74.244421
< 0.1%
-74.242852
< 0.1%
-74.241352
< 0.1%
-74.240841
< 0.1%
-74.239862
< 0.1%
-74.239142
< 0.1%
-74.238032
< 0.1%
-74.230591
< 0.1%
-74.212381
< 0.1%
ValueCountFrequency (%)
-73.705221
 
< 0.1%
-73.705241
 
< 0.1%
-73.710871
 
< 0.1%
-73.712993
< 0.1%
-73.71691
 
< 0.1%
-73.717951
 
< 0.1%
-73.718291
 
< 0.1%
-73.719281
 
< 0.1%
-73.719971
 
< 0.1%
-73.721221
 
< 0.1%

country
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
UNITED STATES
104651 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters1360463
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUNITED STATES
2nd rowUNITED STATES
3rd rowUNITED STATES
4th rowUNITED STATES
5th rowUNITED STATES

Common Values

ValueCountFrequency (%)
UNITED STATES104651
100.0%

Length

2025-10-21T17:27:03.220237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-21T17:27:03.284364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
united104651
50.0%
states104651
50.0%

Most occurring characters

ValueCountFrequency (%)
T313953
23.1%
S209302
15.4%
E209302
15.4%
U104651
 
7.7%
I104651
 
7.7%
N104651
 
7.7%
D104651
 
7.7%
104651
 
7.7%
A104651
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1360463
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T313953
23.1%
S209302
15.4%
E209302
15.4%
U104651
 
7.7%
I104651
 
7.7%
N104651
 
7.7%
D104651
 
7.7%
104651
 
7.7%
A104651
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1360463
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T313953
23.1%
S209302
15.4%
E209302
15.4%
U104651
 
7.7%
I104651
 
7.7%
N104651
 
7.7%
D104651
 
7.7%
104651
 
7.7%
A104651
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1360463
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T313953
23.1%
S209302
15.4%
E209302
15.4%
U104651
 
7.7%
I104651
 
7.7%
N104651
 
7.7%
D104651
 
7.7%
104651
 
7.7%
A104651
 
7.7%

country code
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.1 MiB
US
104651 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters209302
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
US104651
100.0%

Length

2025-10-21T17:27:03.358148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-21T17:27:03.426603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
us104651
100.0%

Most occurring characters

ValueCountFrequency (%)
U104651
50.0%
S104651
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)209302
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U104651
50.0%
S104651
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)209302
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U104651
50.0%
S104651
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)209302
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U104651
50.0%
S104651
50.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size102.3 KiB
False
52620 
True
52031 
ValueCountFrequency (%)
False52620
50.3%
True52031
49.7%
2025-10-21T17:27:03.473938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.6 MiB
MODERATE
35109 
STRICT
34793 
FLEXIBLE
34749 

Length

Max length8
Median length8
Mean length7.3350661
Min length6

Characters and Unicode

Total characters767622
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFLEXIBLE
2nd rowMODERATE
3rd rowFLEXIBLE
4th rowMODERATE
5th rowFLEXIBLE

Common Values

ValueCountFrequency (%)
MODERATE35109
33.5%
STRICT34793
33.2%
FLEXIBLE34749
33.2%

Length

2025-10-21T17:27:03.580763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-21T17:27:03.668314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
moderate35109
33.5%
strict34793
33.2%
flexible34749
33.2%

Most occurring characters

ValueCountFrequency (%)
E139716
18.2%
T104695
13.6%
R69902
9.1%
I69542
9.1%
L69498
9.1%
O35109
 
4.6%
A35109
 
4.6%
D35109
 
4.6%
M35109
 
4.6%
S34793
 
4.5%
Other values (4)139040
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)767622
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E139716
18.2%
T104695
13.6%
R69902
9.1%
I69542
9.1%
L69498
9.1%
O35109
 
4.6%
A35109
 
4.6%
D35109
 
4.6%
M35109
 
4.6%
S34793
 
4.5%
Other values (4)139040
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)767622
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E139716
18.2%
T104695
13.6%
R69902
9.1%
I69542
9.1%
L69498
9.1%
O35109
 
4.6%
A35109
 
4.6%
D35109
 
4.6%
M35109
 
4.6%
S34793
 
4.5%
Other values (4)139040
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)767622
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E139716
18.2%
T104695
13.6%
R69902
9.1%
I69542
9.1%
L69498
9.1%
O35109
 
4.6%
A35109
 
4.6%
D35109
 
4.6%
M35109
 
4.6%
S34793
 
4.5%
Other values (4)139040
18.1%

room type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
ENTIRE HOME/APT
54768 
PRIVATE ROOM
47500 
SHARED ROOM
 
2263
HOTEL ROOM
 
120

Length

Max length15
Median length15
Mean length13.546101
Min length10

Characters and Unicode

Total characters1417613
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowENTIRE HOME/APT
2nd rowPRIVATE ROOM
3rd rowENTIRE HOME/APT
4th rowENTIRE HOME/APT
5th rowENTIRE HOME/APT

Common Values

ValueCountFrequency (%)
ENTIRE HOME/APT54768
52.3%
PRIVATE ROOM47500
45.4%
SHARED ROOM2263
 
2.2%
HOTEL ROOM120
 
0.1%

Length

2025-10-21T17:27:03.772427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-21T17:27:03.858736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
entire54768
26.2%
home/apt54768
26.2%
room49883
23.8%
private47500
22.7%
shared2263
 
1.1%
hotel120
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E214187
15.1%
T157156
11.1%
O154654
10.9%
R154414
10.9%
104651
7.4%
M104651
7.4%
A104531
7.4%
I102268
7.2%
P102268
7.2%
H57151
 
4.0%
Other values (6)161682
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1417613
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E214187
15.1%
T157156
11.1%
O154654
10.9%
R154414
10.9%
104651
7.4%
M104651
7.4%
A104531
7.4%
I102268
7.2%
P102268
7.2%
H57151
 
4.0%
Other values (6)161682
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1417613
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E214187
15.1%
T157156
11.1%
O154654
10.9%
R154414
10.9%
104651
7.4%
M104651
7.4%
A104531
7.4%
I102268
7.2%
P102268
7.2%
H57151
 
4.0%
Other values (6)161682
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1417613
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E214187
15.1%
T157156
11.1%
O154654
10.9%
R154414
10.9%
104651
7.4%
M104651
7.4%
A104531
7.4%
I102268
7.2%
P102268
7.2%
H57151
 
4.0%
Other values (6)161682
11.4%

Construction year
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.4837
Minimum2003
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size817.7 KiB
2025-10-21T17:27:03.955371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2003
Q12008
median2012
Q32017
95-th percentile2022
Maximum2022
Range19
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.7605996
Coefficient of variation (CV)0.0028624329
Kurtosis-1.2036511
Mean2012.4837
Median Absolute Deviation (MAD)5
Skewness0.0066388104
Sum2.1060843 × 108
Variance33.184508
MonotonicityNot monotonic
2025-10-21T17:27:04.083125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
20145339
 
5.1%
20065336
 
5.1%
20085327
 
5.1%
20195308
 
5.1%
20095279
 
5.0%
20105257
 
5.0%
20205247
 
5.0%
20125242
 
5.0%
20225239
 
5.0%
20055236
 
5.0%
Other values (11)51841
49.5%
ValueCountFrequency (%)
20035234
5.0%
20045154
4.9%
20055236
5.0%
20065336
5.1%
20075200
5.0%
20085327
5.1%
20095279
5.0%
20105257
5.0%
20115160
4.9%
20125242
5.0%
ValueCountFrequency (%)
20225239
5.0%
20215141
4.9%
20205247
5.0%
20195308
5.1%
20185151
4.9%
20175171
4.9%
20165102
4.9%
20155195
5.0%
20145339
5.1%
20135116
4.9%

minimum nights
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8433364
Minimum-2.5
Maximum9.5
Zeros0
Zeros (%)0.0%
Negative13
Negative (%)< 0.1%
Memory size817.7 KiB
2025-10-21T17:27:04.220846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2.5
5-th percentile1
Q12
median3
Q35
95-th percentile9.5
Maximum9.5
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0430876
Coefficient of variation (CV)0.79178278
Kurtosis-0.56725282
Mean3.8433364
Median Absolute Deviation (MAD)2
Skewness0.9584989
Sum402209
Variance9.2603822
MonotonicityNot monotonic
2025-10-21T17:27:04.346235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
125941
24.8%
224082
23.0%
9.518739
17.9%
316877
16.1%
46728
 
6.4%
56165
 
5.9%
74122
 
3.9%
61571
 
1.5%
8251
 
0.2%
9162
 
0.2%
Other values (3)13
 
< 0.1%
ValueCountFrequency (%)
-2.511
 
< 0.1%
-21
 
< 0.1%
-11
 
< 0.1%
125941
24.8%
224082
23.0%
316877
16.1%
46728
 
6.4%
56165
 
5.9%
61571
 
1.5%
74122
 
3.9%
ValueCountFrequency (%)
9.518739
17.9%
9162
 
0.2%
8251
 
0.2%
74122
 
3.9%
61571
 
1.5%
56165
 
5.9%
46728
 
6.4%
316877
16.1%
224082
23.0%
125941
24.8%

number of reviews
Real number (ℝ)

High correlation  Zeros 

Distinct476
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.441343
Minimum0
Maximum1024
Zeros16045
Zeros (%)15.3%
Negative0
Negative (%)0.0%
Memory size817.7 KiB
2025-10-21T17:27:04.481311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q330
95-th percentile125
Maximum1024
Range1024
Interquartile range (IQR)29

Descriptive statistics

Standard deviation49.391322
Coefficient of variation (CV)1.7998872
Kurtosis24.893079
Mean27.441343
Median Absolute Deviation (MAD)7
Skewness3.8299982
Sum2871764
Variance2439.5027
MonotonicityNot monotonic
2025-10-21T17:27:04.629680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016045
 
15.3%
110600
 
10.1%
27318
 
7.0%
35481
 
5.2%
44236
 
4.0%
53494
 
3.3%
62986
 
2.9%
72766
 
2.6%
82413
 
2.3%
92152
 
2.1%
Other values (466)47160
45.1%
ValueCountFrequency (%)
016045
15.3%
110600
10.1%
27318
7.0%
35481
 
5.2%
44236
 
4.0%
53494
 
3.3%
62986
 
2.9%
72766
 
2.6%
82413
 
2.3%
92152
 
2.1%
ValueCountFrequency (%)
10241
< 0.1%
10101
< 0.1%
9661
< 0.1%
8841
< 0.1%
8491
< 0.1%
7971
< 0.1%
7761
< 0.1%
7381
< 0.1%
6981
< 0.1%
6791
< 0.1%

reviews per month
Real number (ℝ)

High correlation 

Distinct1016
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2766491
Minimum0.01
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size817.7 KiB
2025-10-21T17:27:04.779011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.05
Q10.28
median0.74
Q31.71
95-th percentile4.32
Maximum90
Range89.99
Interquartile range (IQR)1.43

Descriptive statistics

Standard deviation1.621826
Coefficient of variation (CV)1.2703773
Kurtosis244.2229
Mean1.2766491
Median Absolute Deviation (MAD)0.55
Skewness7.5021525
Sum133602.6
Variance2.6303194
MonotonicityNot monotonic
2025-10-21T17:27:04.919130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7416421
 
15.7%
0.031694
 
1.6%
0.051522
 
1.5%
11486
 
1.4%
0.091296
 
1.2%
0.041296
 
1.2%
0.161282
 
1.2%
0.081279
 
1.2%
0.061221
 
1.2%
0.021181
 
1.1%
Other values (1006)75973
72.6%
ValueCountFrequency (%)
0.0167
 
0.1%
0.021181
1.1%
0.031694
1.6%
0.041296
1.2%
0.051522
1.5%
0.061221
1.2%
0.071102
1.1%
0.081279
1.2%
0.091296
1.2%
0.11009
1.0%
ValueCountFrequency (%)
901
< 0.1%
84.491
< 0.1%
65.741
< 0.1%
58.52
< 0.1%
57.311
< 0.1%
47.111
< 0.1%
44.631
< 0.1%
34.461
< 0.1%
33.081
< 0.1%
30.511
< 0.1%

review rate number
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2797024
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size817.7 KiB
2025-10-21T17:27:05.031965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2829167
Coefficient of variation (CV)0.39116862
Kurtosis-1.1213596
Mean3.2797024
Median Absolute Deviation (MAD)1
Skewness-0.13952912
Sum343224.14
Variance1.6458752
MonotonicityNot monotonic
2025-10-21T17:27:05.135306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
523865
22.8%
423797
22.7%
323705
22.7%
223553
22.5%
19398
 
9.0%
3.279702448333
 
0.3%
ValueCountFrequency (%)
19398
 
9.0%
223553
22.5%
323705
22.7%
3.279702448333
 
0.3%
423797
22.7%
523865
22.8%
ValueCountFrequency (%)
523865
22.8%
423797
22.7%
3.279702448333
 
0.3%
323705
22.7%
223553
22.5%
19398
 
9.0%
Distinct78
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9226668
Minimum1
Maximum332
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size817.7 KiB
2025-10-21T17:27:05.287304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile28.5
Maximum332
Range331
Interquartile range (IQR)1

Descriptive statistics

Standard deviation32.216562
Coefficient of variation (CV)4.0663786
Kurtosis58.833937
Mean7.9226668
Median Absolute Deviation (MAD)0
Skewness7.2313897
Sum829115
Variance1037.9069
MonotonicityNot monotonic
2025-10-21T17:27:05.432974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165024
62.1%
214715
 
14.1%
36715
 
6.4%
43637
 
3.5%
52037
 
1.9%
61506
 
1.4%
71011
 
1.0%
8975
 
0.9%
9559
 
0.5%
10523
 
0.5%
Other values (68)7949
 
7.6%
ValueCountFrequency (%)
165024
62.1%
214715
 
14.1%
36715
 
6.4%
43637
 
3.5%
52037
 
1.9%
61506
 
1.4%
71011
 
1.0%
8975
 
0.9%
9559
 
0.5%
10523
 
0.5%
ValueCountFrequency (%)
33241
 
< 0.1%
327483
0.5%
232274
0.3%
21830
 
< 0.1%
208211
0.2%
186154
 
0.1%
17166
 
0.1%
161117
 
0.1%
15257
 
0.1%
12655
 
0.1%

availability 365
Real number (ℝ)

Zeros 

Distinct438
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.03003
Minimum-10
Maximum3677
Zeros24007
Zeros (%)22.9%
Negative438
Negative (%)0.4%
Memory size817.7 KiB
2025-10-21T17:27:05.588326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile0
Q13
median97
Q3268
95-th percentile365
Maximum3677
Range3687
Interquartile range (IQR)265

Descriptive statistics

Standard deviation135.18523
Coefficient of variation (CV)0.95855632
Kurtosis3.1917403
Mean141.03003
Median Absolute Deviation (MAD)97
Skewness0.64390263
Sum14758934
Variance18275.046
MonotonicityNot monotonic
2025-10-21T17:27:05.733220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
024007
 
22.9%
3652553
 
2.4%
3641187
 
1.1%
89771
 
0.7%
1745
 
0.7%
179698
 
0.7%
90687
 
0.7%
5639
 
0.6%
97594
 
0.6%
3592
 
0.6%
Other values (428)72178
69.0%
ValueCountFrequency (%)
-1038
< 0.1%
-948
< 0.1%
-850
< 0.1%
-737
< 0.1%
-632
< 0.1%
-559
0.1%
-442
< 0.1%
-343
< 0.1%
-243
< 0.1%
-146
< 0.1%
ValueCountFrequency (%)
36771
 
< 0.1%
42647
< 0.1%
42551
< 0.1%
42438
< 0.1%
42349
< 0.1%
42248
< 0.1%
42143
< 0.1%
42041
< 0.1%
41945
< 0.1%
41849
< 0.1%

price_clean
Real number (ℝ)

High correlation 

Distinct1152
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean625.31834
Minimum50
Maximum1200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size817.7 KiB
2025-10-21T17:27:05.879533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile108
Q1340.5
median625.31834
Q3912
95-th percentile1143
Maximum1200
Range1150
Interquartile range (IQR)571.5

Descriptive statistics

Standard deviation331.20697
Coefficient of variation (CV)0.52966138
Kurtosis-1.1897256
Mean625.31834
Median Absolute Deviation (MAD)285.68166
Skewness0.0010186731
Sum65440190
Variance109698.06
MonotonicityNot monotonic
2025-10-21T17:27:06.034827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
625.3183425254
 
0.2%
206141
 
0.1%
1056137
 
0.1%
481130
 
0.1%
833129
 
0.1%
573128
 
0.1%
972128
 
0.1%
138124
 
0.1%
430122
 
0.1%
406122
 
0.1%
Other values (1142)103236
98.6%
ValueCountFrequency (%)
5095
0.1%
5188
0.1%
5290
0.1%
5366
0.1%
5468
0.1%
55100
0.1%
56109
0.1%
5788
0.1%
5883
0.1%
59112
0.1%
ValueCountFrequency (%)
120071
0.1%
1199102
0.1%
119880
0.1%
119780
0.1%
119679
0.1%
119595
0.1%
119477
0.1%
119374
0.1%
119272
0.1%
119192
0.1%

last review_date
Date

Missing 

Distinct2477
Distinct (%)2.8%
Missing16206
Missing (%)15.5%
Memory size817.7 KiB
Minimum2012-07-11 00:00:00
Maximum2058-06-16 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-21T17:27:06.199310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:27:06.397955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

service fee_cleaned
Real number (ℝ)

High correlation 

Distinct232
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.03086
Minimum10
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size817.7 KiB
2025-10-21T17:27:06.582253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile22
Q168
median125
Q3182
95-th percentile229
Maximum240
Range230
Interquartile range (IQR)114

Descriptive statistics

Standard deviation66.225902
Coefficient of variation (CV)0.52967645
Kurtosis-1.1887984
Mean125.03086
Median Absolute Deviation (MAD)57
Skewness0.0016650957
Sum13084605
Variance4385.87
MonotonicityNot monotonic
2025-10-21T17:27:06.753180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41543
 
0.5%
216537
 
0.5%
81531
 
0.5%
177531
 
0.5%
57520
 
0.5%
207515
 
0.5%
128512
 
0.5%
127511
 
0.5%
178508
 
0.5%
92506
 
0.5%
Other values (222)99437
95.0%
ValueCountFrequency (%)
10273
0.3%
11431
0.4%
12441
0.4%
13439
0.4%
14454
0.4%
15461
0.4%
16502
0.5%
17422
0.4%
18432
0.4%
19442
0.4%
ValueCountFrequency (%)
240253
0.2%
239405
0.4%
238467
0.4%
237479
0.5%
236430
0.4%
235448
0.4%
234473
0.5%
233446
0.4%
232428
0.4%
231468
0.4%

Interactions

2025-10-21T17:26:54.332346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:29.353829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:32.447335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:34.330696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:35.997369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:37.953999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:39.914682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:42.118604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:44.701321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:46.429452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:48.374331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:50.567422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:52.443987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:54.517955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:30.049843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:32.578552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:34.450378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:36.126646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:38.087938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:40.058206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:42.313483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:44.838339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:46.572555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:48.524488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:50.701143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:52.589175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:54.725117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:30.651341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:32.711181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:34.567879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:36.256016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:38.221956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:40.192461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:42.492074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:44.970576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:46.713083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:48.664241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:50.840060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:52.716172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:54.916247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:31.154424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:32.828213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:34.698937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:36.374037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:38.362954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:40.335662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:42.659462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:45.097287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:46.852991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:48.805345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:50.965884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:52.848115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:55.098550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:31.283970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:32.948204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:34.821451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:36.503083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:38.503016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:40.483031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:42.843607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:45.235300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:46.994831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:48.939553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:51.099091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:52.978876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:55.304872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:31.422427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:33.081661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:34.959733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:36.640413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:38.655035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:40.643127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:43.052349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:45.378346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:47.155034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:49.094968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:51.243633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:53.121713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:55.485914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:31.562890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:33.207691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:35.081129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:36.787465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:38.828276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:40.782617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:43.275028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:45.505092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:47.302846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:49.244948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:51.381398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:53.253085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:55.670794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:31.688543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:33.335813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:35.218318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:36.914251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:38.975348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:40.942022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:43.463607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:45.641862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:47.462829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:49.375317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:51.532954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:53.382890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:55.881696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:31.807402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:33.668648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:35.339082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:37.044037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:39.121898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:41.070552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:44.018034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:45.764124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:47.607468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:49.524082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:51.663510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:53.511438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:56.105690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:31.942847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:33.799920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:35.469781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:37.187732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:39.276235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:41.300733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:44.185350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:45.896096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:47.762990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:49.666886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:51.853988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:53.669368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:56.294854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:32.062023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:33.933778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:35.598455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:37.325418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:39.424895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:41.511019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:44.319242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:46.023839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:47.915331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:49.805769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:52.016725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:53.802600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:56.495881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:32.194856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:34.068676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:35.746495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:37.451179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:39.580920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:41.719736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:44.450056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:46.157748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:48.065857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:49.943056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:52.154350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:53.940569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:56.712193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:32.325004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:34.199752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:35.868628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:37.580490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:39.746982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:41.906002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:44.574092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:46.307058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:48.208535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:50.077458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:52.305888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-21T17:26:54.136776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-21T17:27:06.916040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Construction yearavailability 365calculated host listings countcancellation_policyhost idhost_identity_verifiedidinstant_bookablelatlongminimum nightsneighbourhood groupnumber of reviewsprice_cleanreview rate numberreviews per monthroom typeservice fee_cleaned
Construction year1.000-0.006-0.0050.0030.0050.0000.0010.0000.0040.001-0.0030.0090.002-0.0050.0050.0030.005-0.004
availability 365-0.0061.0000.2960.000-0.0030.003-0.1550.000-0.0010.0370.0300.0320.136-0.003-0.0030.1530.046-0.003
calculated host listings count-0.0050.2961.0000.0030.0030.0000.0580.0000.0250.0470.0640.0880.0530.0010.0350.0920.0770.002
cancellation_policy0.0030.0000.0031.0000.0000.0000.0000.0070.0040.0000.0000.0030.0000.0000.0000.0000.0000.000
host id0.005-0.0030.0030.0001.0000.007-0.0010.0000.001-0.009-0.0000.004-0.0060.0030.003-0.0040.0060.003
host_identity_verified0.0000.0030.0000.0000.0071.0000.0000.0000.0000.0000.0000.0000.0070.0000.0080.0000.0000.000
id0.001-0.1550.0580.000-0.0010.0001.0000.003-0.0090.0400.0190.0480.0080.0070.0330.0560.0660.007
instant_bookable0.0000.0000.0000.0070.0000.0000.0031.0000.0050.0050.0040.0000.0000.0080.0000.0000.0000.005
lat0.004-0.0010.0250.0040.0010.000-0.0090.0051.0000.0330.0300.440-0.052-0.006-0.003-0.0370.089-0.006
long0.0010.0370.0470.000-0.0090.0000.0400.0050.0331.000-0.1100.5380.0910.0040.0110.1090.1190.004
minimum nights-0.0030.0300.0640.000-0.0000.0000.0190.0040.030-0.1101.0000.067-0.170-0.0040.003-0.2940.144-0.004
neighbourhood group0.0090.0320.0880.0030.0040.0000.0480.0000.4400.5380.0671.0000.0230.0070.0170.0270.0940.006
number of reviews0.0020.1360.0530.000-0.0060.0070.0080.000-0.0520.091-0.1700.0231.0000.005-0.0130.5700.0640.005
price_clean-0.005-0.0030.0010.0000.0030.0000.0070.008-0.0060.004-0.0040.0070.0051.000-0.0050.0030.0110.998
review rate number0.005-0.0030.0350.0000.0030.0080.0330.000-0.0030.0110.0030.017-0.013-0.0051.0000.0450.008-0.005
reviews per month0.0030.1530.0920.000-0.0040.0000.0560.000-0.0370.109-0.2940.0270.5700.0030.0451.0000.0840.003
room type0.0050.0460.0770.0000.0060.0000.0660.0000.0890.1190.1440.0940.0640.0110.0080.0841.0000.010
service fee_cleaned-0.004-0.0030.0020.0000.0030.0000.0070.005-0.0060.004-0.0040.0060.0050.998-0.0050.0030.0101.000

Missing values

2025-10-21T17:26:57.070536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-21T17:26:57.534451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-21T17:26:58.116546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idNAMEhost idhost_identity_verifiedhost nameneighbourhood groupneighbourhoodlatlongcountrycountry codeinstant_bookablecancellation_policyroom typeConstruction yearminimum nightsnumber of reviewsreviews per monthreview rate numbercalculated host listings countavailability 365price_cleanlast review_dateservice fee_cleaned
014742560CLASSIC ARTIST LOFT97210996539VERIFIEDJOSHBROOKLYNCOLUMBIA ST40.68626-74.00141UNITED STATESUSTrueFLEXIBLEENTIRE HOME/APT2013.04.09.00.385.01.074.0616.02019-06-26123.0
155508928ÉTAGE AU SEIN D’UN DUPLEX À BROOKLYN33249221187UNCONFIRMEDSOPHIE & GRÉGOIREBROOKLYNCLINTON HILL40.68227-73.96486UNITED STATESUSTrueMODERATEPRIVATE ROOM2005.02.09.00.892.01.017.01095.02019-06-30219.0
230561537AMAZING VIEW IN ULTRA LUXURY MIDTOWN APARTMENT!63109258528VERIFIEDMAYMANHATTANHELL'S KITCHEN40.75275-73.99330UNITED STATESUSFalseFLEXIBLEENTIRE HOME/APT2015.01.028.03.644.06.0334.0969.02022-02-17194.0
313914662HARLEM HOME AWAY FROM HOME!42768739399VERIFIEDARIELLEMANHATTANHARLEM40.82843-73.94672UNITED STATESUSTrueMODERATEENTIRE HOME/APT2021.03.04.00.161.01.00.0778.02019-06-25156.0
49063811COZY LOFT IN FLUSHING41428819097VERIFIEDERJONQUEENSFLUSHING40.75485-73.82138UNITED STATESUSFalseFLEXIBLEENTIRE HOME/APT2009.03.01.00.021.01.0368.0815.02016-03-25163.0
522883462REAL BROOKLYN APT ON PACIFIC AND NEW YORK1508400733UNCONFIRMEDMATTBROOKLYNCROWN HEIGHTS40.67683-73.94702UNITED STATESUSFalseMODERATEPRIVATE ROOM2007.05.02.00.324.01.0220.0172.02019-02-0134.0
638229670PRIME, SPACIOUS WEST VILLAGE APARTMENT22981236558VERIFIEDJORDANMANHATTANGREENWICH VILLAGE40.73129-74.00097UNITED STATESUSTrueFLEXIBLEPRIVATE ROOM2017.09.561.01.954.01.00.01181.02020-04-05236.0
726509864PRIVATE, NEWLY RENOVATED BEDROOM, 10 MINS FROM JFK82159107182UNCONFIRMEDMINAQUEENSHOWARD BEACH40.66635-73.85258UNITED STATESUSTrueSTRICTPRIVATE ROOM2003.01.03.02.142.04.0329.0320.02019-07-0664.0
855141648BRIGHT WILLIAMSBURG ROOM WITH HUGE PRIVATE TERRACE40990182521UNCONFIRMEDBENBROOKLYNWILLIAMSBURG40.71247-73.94548UNITED STATESUSTrueSTRICTPRIVATE ROOM2003.01.02.00.185.01.00.0986.02018-09-05197.0
99103577CHARMING PREWAR UPPER WEST SIDE APT87433243922VERIFIEDDANIELMANHATTANUPPER WEST SIDE40.79828-73.97166UNITED STATESUSFalseFLEXIBLEENTIRE HOME/APT2013.01.00.00.741.01.095.0714.0NaN143.0
idNAMEhost idhost_identity_verifiedhost nameneighbourhood groupneighbourhoodlatlongcountrycountry codeinstant_bookablecancellation_policyroom typeConstruction yearminimum nightsnumber of reviewsreviews per monthreview rate numbercalculated host listings countavailability 365price_cleanlast review_dateservice fee_cleaned
10464125374887BIG ROOM PRIME LOCATION WAHE UPTOWN MANHATTAN48898220621VERIFIEDMAJSANMANHATTANWASHINGTON HEIGHTS40.84387-73.93969UNITED STATESUSTrueSTRICTPRIVATE ROOM2006.01.03.01.643.0000001.0293.01040.02019-06-11208.0
10464249326481LUXURY ROOM (RM3), 5 MINS FROM COLUMBIA49177551774VERIFIEDEUGENEMANHATTANMORNINGSIDE HEIGHTS40.81097-73.96001UNITED STATESUSFalseMODERATEPRIVATE ROOM2020.03.068.01.885.0000006.0128.0167.02019-06-2433.0
10464321543582NIGHTS IN MIDTOWN MANHATTAN-LADIES ONLY510689754UNCONFIRMEDMELANIEMANHATTANKIPS BAY40.74579-73.97887UNITED STATESUSFalseFLEXIBLESHARED ROOM2007.02.06.00.742.0000003.0352.0221.02019-04-2144.0
10464446503124GIGI'S STUNNING PLACE32899803868UNCONFIRMEDGLORIABROOKLYNEAST NEW YORK40.67118-73.86821UNITED STATESUSTrueMODERATEENTIRE HOME/APT2016.01.042.01.795.0000001.0348.01077.02019-06-30215.0
1046454461496ZEN DEN & MODERN OUTDOOR SPACE10269939573UNCONFIRMEDCARMENBROOKLYNCYPRESS HILLS40.68711-73.87336UNITED STATESUSTrueFLEXIBLEENTIRE HOME/APT2010.03.0114.02.023.0000001.0217.0768.02019-06-24154.0
10464631314874AWESOME PRIVATE ROOM WITH WIFI & SELF CHECK-IN47647391192VERIFIEDCAROLBROOKLYNPROSPECT-LEFFERTS GARDENS40.66028-73.94217UNITED STATESUSFalseSTRICTPRIVATE ROOM2021.01.014.04.003.00000017.0179.01065.02022-02-11213.0
10464743429022SERENITY NEAR TIME SQUARE8913095036UNCONFIRMEDROSHELLEMANHATTANHELL'S KITCHEN40.75764-73.98963UNITED STATESUSFalseFLEXIBLEPRIVATE ROOM2013.02.00.00.745.0000001.00.0174.0NaN35.0
10464838167812⚡STYLISH APT IN TRENDY LOCATION!! ⭐85414163687UNCONFIRMEDSITOMANHATTANCHELSEA40.73895-73.99837UNITED STATESUSTrueFLEXIBLEENTIRE HOME/APT2016.09.547.01.384.0000001.00.0450.02020-05-2890.0
1046491476314FULLY FURNISHED 1B/1BTH UWS GEM 1 YR SUBLEASE44588815342VERIFIEDSTEWARTMANHATTANUPPER WEST SIDE40.78012-73.98439UNITED STATESUSTrueMODERATEENTIRE HOME/APT2007.01.00.00.743.2797021.0302.0316.0NaN63.0
1046509724915HAMILITON HEIGHTS HOME68074132275VERIFIEDLAURENMANHATTANHARLEM40.81934-73.95668UNITED STATESUSTrueSTRICTPRIVATE ROOM2017.02.00.00.744.0000001.0101.0692.0NaN138.0

Duplicate rows

Most frequently occurring

idNAMEhost idhost_identity_verifiedhost nameneighbourhood groupneighbourhoodlatlongcountrycountry codeinstant_bookablecancellation_policyroom typeConstruction yearminimum nightsnumber of reviewsreviews per monthreview rate numbercalculated host listings countavailability 365price_cleanlast review_dateservice fee_cleaned# duplicates
99320387618SONDER | 116 JOHN | COZY 2BR+ GYM1165490779UNCONFIRMEDSONDERMANHATTANFINANCIAL DISTRICT40.70797-74.00550UNITED STATESUSFalseMODERATEENTIRE HOME/APT2008.09.51.00.262.096.0311.0203.02019-03-1641.04
2306049910THE MOST EFFICIENT EFFICIENCY EVER45601026524UNCONFIRMEDGREGBROOKLYNWILLIAMSBURG40.70458-73.94095UNITED STATESUSFalseSTRICTENTIRE HOME/APT2012.02.015.00.312.01.0210.0719.02019-07-01144.03
2956085810PRIVATE ROOM IN THE BEST PART OF BK4746552394VERIFIEDBRADBROOKLYNCROWN HEIGHTS40.66743-73.94712UNITED STATESUSFalseFLEXIBLEPRIVATE ROOM2011.07.049.01.012.01.0425.0516.02019-06-01103.03
58813970997SUNNY SPACIOUS 2 BEDROOM CONDO63177292896UNCONFIRMEDMICHELLEBROOKLYNCANARSIE40.62986-73.90638UNITED STATESUSTrueMODERATEPRIVATE ROOM2012.02.045.01.784.01.041.0658.02019-06-30132.03
82920297041FAM-FRIENDLY BROOKLYN BROWNSTONE W. VINTAGE CHARM1570276266UNCONFIRMEDASHLEYBROOKLYNBEDFORD-STUYVESANT40.68398-73.93464UNITED STATESUSTrueMODERATEENTIRE HOME/APT2019.02.06.02.024.01.00.0979.02019-06-27196.03
84120303669BROOKLYN PRIVATE APARTMENT27593217400VERIFIEDMARIABROOKLYNBOROUGH PARK40.64480-73.99524UNITED STATESUSTrueMODERATEENTIRE HOME/APT2017.02.05.00.524.01.03.0385.02019-01-0477.03
84920308087HARLEM GEM97528248465UNCONFIRMEDERICAMANHATTANEAST HARLEM40.80147-73.94432UNITED STATESUSTrueMODERATEENTIRE HOME/APT2020.03.02.00.193.01.00.0813.02018-12-10163.03
86420316372PARK AVENUE HAVEN46677962999VERIFIEDANNAMANHATTANEAST HARLEM40.78954-73.95076UNITED STATESUSFalseFLEXIBLEPRIVATE ROOM2004.01.035.03.904.01.090.0362.02019-07-0572.03
87620322999WILLIAMSBURG LOFT - SPACIOUS, SKY-HIGH CEILINGS32108646910VERIFIEDJAYBROOKLYNWILLIAMSBURG40.71095-73.95171UNITED STATESUSFalseMODERATEPRIVATE ROOM2005.09.50.00.745.02.0157.0946.0NaN189.03
90720340121COZY CORNER NEAR EMPIRE STATE BUILDING40725508872VERIFIEDMARILUZMANHATTANMIDTOWN40.74858-73.98341UNITED STATESUSTrueMODERATESHARED ROOM2010.01.0112.010.775.03.00.0152.02019-06-1330.03